# Memoization (1D, 2D and 3D)

Most of the Dynamic Programming problems are solved in two ways:

**Tabulation:**Bottom Up**Memoization:**Top Down

One of the easier approaches to solve most of the problems in DP is to write the recursive code at first and then write the Bottom-up Tabulation Method or Top-down Memoization of the recursive function. The steps to write the DP solution of Top-down approach to any problem is to:

- Write the recursive code
- Memoize the return value and use it to reduce recursive calls.

**1-D Memoization**

The first step will be to write the recursive code. In the program below, a program related to recursion where only one parameter changes its value has been shown. Since only one parameter is non-constant, this method is known as 1-D memoization. E.g., the Fibonacci series problem to find the N-th term in the Fibonacci series. The recursive approach has been discussed over here.

Given below is the recursive code to find the N-th term:

## C++

`// C++ program to find the Nth term ` `// of Fibonacci series ` `#include <bits/stdc++.h> ` `using` `namespace` `std; ` ` ` `// Fibonacci Series using Recursion ` `int` `fib(` `int` `n) ` `{ ` ` ` ` ` `// Base case ` ` ` `if` `(n <= 1) ` ` ` `return` `n; ` ` ` ` ` `// recursive calls ` ` ` `return` `fib(n - 1) + fib(n - 2); ` `} ` ` ` `// Driver Code ` `int` `main() ` `{ ` ` ` `int` `n = 6; ` ` ` `printf` `(` `"%d"` `, fib(n)); ` ` ` `return` `0; ` `} ` |

*chevron_right*

*filter_none*

## Java

`// Java program to find the ` `// Nth term of Fibonacci series ` `import` `java.io.*; ` ` ` `class` `GFG ` `{ ` ` ` `// Fibonacci Series ` `// using Recursion ` `static` `int` `fib(` `int` `n) ` `{ ` ` ` ` ` `// Base case ` ` ` `if` `(n <= ` `1` `) ` ` ` `return` `n; ` ` ` ` ` `// recursive calls ` ` ` `return` `fib(n - ` `1` `) + ` ` ` `fib(n - ` `2` `); ` `} ` ` ` `// Driver Code ` `public` `static` `void` `main (String[] args) ` `{ ` ` ` `int` `n = ` `6` `; ` ` ` `System.out.println(fib(n)); ` `} ` `} ` ` ` `// This code is contributed ` `// by ajit ` |

*chevron_right*

*filter_none*

## Python3

`# Python3 program to find the Nth term ` `# of Fibonacci series ` ` ` `# Fibonacci Series using Recursion ` `def` `fib(n): ` ` ` ` ` ` ` `# Base case ` ` ` `if` `(n <` `=` `1` `): ` ` ` `return` `n ` ` ` ` ` `# recursive calls ` ` ` `return` `fib(n ` `-` `1` `) ` `+` `fib(n ` `-` `2` `) ` ` ` `# Driver Code ` `if` `__name__` `=` `=` `'__main__'` `: ` ` ` `n ` `=` `6` ` ` `print` `(fib(n)) ` ` ` `# This code is contributed by ` `# Shivi_Aggarwal ` |

*chevron_right*

*filter_none*

## C#

`// C# program to find ` `// the Nth term of ` `// Fibonacci series ` `using` `System; ` ` ` `class` `GFG ` `{ ` ` ` `// Fibonacci Series ` `// using Recursion ` `static` `int` `fib(` `int` `n) ` `{ ` ` ` `// Base case ` ` ` `if` `(n <= 1) ` ` ` `return` `n; ` ` ` ` ` `// recursive calls ` ` ` `return` `fib(n - 1) + ` ` ` `fib(n - 2); ` `} ` `// Driver Code ` `static` `public` `void` `Main () ` `{ ` ` ` `int` `n = 6; ` ` ` `Console.WriteLine(fib(n)); ` `} ` `} ` ` ` `// This code is contributed ` `// by akt_mit ` |

*chevron_right*

*filter_none*

## PHP

`<?php ` `// PHP program to find ` `// the Nth term of ` `// Fibonacci series ` `// using Recursion ` ` ` `function` `fib(` `$n` `) ` `{ ` ` ` ` ` `// Base case ` ` ` `if` `(` `$n` `<= 1) ` ` ` `return` `$n` `; ` ` ` ` ` `// recursive calls ` ` ` `return` `fib(` `$n` `- 1) + ` ` ` `fib(` `$n` `- 2); ` `} ` ` ` `// Driver Code ` `$n` `= 6; ` `echo` `fib(` `$n` `); ` ` ` `// This code is contributed ` `// by ajit ` `?> ` |

*chevron_right*

*filter_none*

**Output:**

8

A common observation is that this implementation does a lot of **repeated work** (see the following recursion tree). So this will consume a lot of time for finding the N-th Fibonacci number if done.

fib(5) / \ fib(4) fib(3) / \ / \ fib(3) fib(2) fib(2) fib(1) / \ / \ / \ fib(2) fib(1) fib(1) fib(0) fib(1) fib(0) / \ fib(1) fib(0) In the above tree fib(3), fib(2), fib(1), fib(0) all are called more then once.

The following problem has been solved using Tabulation method.

In the program below, the steps to write a **Top-Down approach program** has been explained. Some modifications in the recursive program will reduce the complexity of the program and give the desired result. If fib(x) has not occurred previously, then we store the value of **fib(x) in an array term at index x and return term[x]**. By memoizing the return value of fib(x) at index x of an array, reduce the number of recursive calls at the next step when fib(x) has already been called. So without doing further recursive calls to compute the value of fib(x), **return term[x] when fib(x)** has already been computed previously to avoid a lot of repeated work as shown in the tree.

Given below is the memoized recursive code to find the N-th term.

## C++

`// CPP program to find the Nth term ` `// of Fibonacci series ` `#include <bits/stdc++.h> ` `using` `namespace` `std; ` `int` `term[1000]; ` `// Fibonacci Series using memoized Recursion ` `int` `fib(` `int` `n) ` `{ ` ` ` `// base case ` ` ` `if` `(n <= 1) ` ` ` `return` `n; ` ` ` ` ` `// if fib(n) has already been computed ` ` ` `// we do not do further recursive calls ` ` ` `// and hence reduce the number of repeated ` ` ` `// work ` ` ` `if` `(term[n] != 0) ` ` ` `return` `term[n]; ` ` ` ` ` `else` `{ ` ` ` ` ` `// store the computed value of fib(n) ` ` ` `// in an array term at index n to ` ` ` `// so that it does not needs to be ` ` ` `// precomputed again ` ` ` `term[n] = fib(n - 1) + fib(n - 2); ` ` ` ` ` `return` `term[n]; ` ` ` `} ` `} ` ` ` `// Driver Code ` `int` `main() ` `{ ` ` ` `int` `n = 6; ` ` ` `printf` `(` `"%d"` `, fib(n)); ` ` ` `return` `0; ` `} ` |

*chevron_right*

*filter_none*

## Java

`// Java program to find ` `// the Nth term of ` `// Fibonacci series ` `import` `java.io.*; ` ` ` `class` `GFG ` `{ ` ` ` `// Fibonacci Series using ` `// memoized Recursion ` `static` `int` `fib(` `int` `n) ` `{ ` ` ` `int` `[]term = ` `new` `int` `[` `1000` `]; ` ` ` ` ` `// base case ` ` ` `if` `(n <= ` `1` `) ` ` ` `return` `n; ` ` ` ` ` `// if fib(n) has already ` ` ` `// been computed we do not ` ` ` `// do further recursive ` ` ` `// calls and hence reduce ` ` ` `// the number of repeated ` ` ` `// work ` ` ` `if` `(term[n] != ` `0` `) ` ` ` `return` `term[n]; ` ` ` ` ` `else` ` ` `{ ` ` ` ` ` `// store the computed value ` ` ` `// of fib(n) in an array ` ` ` `// term at index n to so that ` ` ` `// it does not needs to be ` ` ` `// precomputed again ` ` ` `term[n] = fib(n - ` `1` `) + ` ` ` `fib(n - ` `2` `); ` ` ` ` ` `return` `term[n]; ` ` ` `} ` `} ` ` ` `// Driver Code ` `public` `static` `void` `main (String[] args) ` `{ ` ` ` `int` `n = ` `6` `; ` ` ` `System.out.println(fib(n)); ` `} ` `} ` ` ` `// This code is contributed by ajit ` |

*chevron_right*

*filter_none*

## C#

`// C# program to find ` `// the Nth term of ` `// Fibonacci series ` ` ` `using` `System; ` `class` `GFG ` `{ ` ` ` `// Fibonacci Series using ` `// memoized Recursion ` `static` `int` `fib(` `int` `n) ` `{ ` ` ` `int` `[] term = ` `new` `int` `[1000]; ` ` ` ` ` `// base case ` ` ` `if` `(n <= 1) ` ` ` `return` `n; ` ` ` ` ` `// if fib(n) has already ` ` ` `// been computed we do not ` ` ` `// do further recursive ` ` ` `// calls and hence reduce ` ` ` `// the number of repeated ` ` ` `// work ` ` ` `if` `(term[n] != 0) ` ` ` `return` `term[n]; ` ` ` ` ` `else` ` ` `{ ` ` ` ` ` `// store the computed value ` ` ` `// of fib(n) in an array ` ` ` `// term at index n to so that ` ` ` `// it does not needs to be ` ` ` `// precomputed again ` ` ` `term[n] = fib(n - 1) + ` ` ` `fib(n - 2); ` ` ` ` ` `return` `term[n]; ` ` ` `} ` `} ` ` ` `// Driver Code ` `public` `static` `void` `Main () ` `{ ` ` ` `int` `n = 6; ` ` ` `Console.Write(fib(n)); ` `} ` `} ` |

*chevron_right*

*filter_none*

**Output:**

8

If the recursive code has been written once, then memoization is just modifying the recursive program and storing the return values to avoid repetitive calls of functions which have been computed previously.

**2-D Memoization**

In the above program, the recursive function had only **one argument whose value was not constant** after every function call. Below, an implementation where the recursive program has two non-constant arguments has been shown.

For e.g., Program to solve the standard Dynamic Problem LCS problem when two strings are given. The general recursive solution of the problem is to generate all subsequences of both given sequences and find the longest matching subsequence. Total possible combinations will be 2^{n}. Hence recursive solution will take **O(2 ^{n})**. The approach to write the recursive solution has been discussed here.

Given below is the recursive solution to the LCS problem:

## C++

`// A Naive recursive implementation of LCS problem ` `#include <bits/stdc++.h> ` ` ` `int` `max(` `int` `a, ` `int` `b); ` ` ` `// Returns length of LCS for X[0..m-1], Y[0..n-1] ` `int` `lcs(` `char` `* X, ` `char` `* Y, ` `int` `m, ` `int` `n) ` `{ ` ` ` `if` `(m == 0 || n == 0) ` ` ` `return` `0; ` ` ` `if` `(X[m - 1] == Y[n - 1]) ` ` ` `return` `1 + lcs(X, Y, m - 1, n - 1); ` ` ` `else` ` ` `return` `max(lcs(X, Y, m, n - 1), ` ` ` `lcs(X, Y, m - 1, n)); ` `} ` ` ` `// Utility function to get max of 2 integers ` `int` `max(` `int` `a, ` `int` `b) ` `{ ` ` ` `return` `(a > b) ? a : b; ` `} ` ` ` `// Driver Code ` `int` `main() ` `{ ` ` ` `char` `X[] = ` `"AGGTAB"` `; ` ` ` `char` `Y[] = ` `"GXTXAYB"` `; ` ` ` ` ` `int` `m = ` `strlen` `(X); ` ` ` `int` `n = ` `strlen` `(Y); ` ` ` ` ` `printf` `(` `"Length of LCS is %dn"` `, lcs(X, Y, m, n)); ` ` ` ` ` `return` `0; ` `} ` |

*chevron_right*

*filter_none*

**Output:**

Length of LCS is 4n

Output:

Length of LCS is 4

Considering the above implementation, the following is a partial recursion tree for input strings “AXYT” and “AYZX”

lcs("AXYT", "AYZX") / \ lcs("AXY", "AYZX") lcs("AXYT", "AYZ") / \ / \ lcs("AX", "AYZX") lcs("AXY", "AYZ") lcs("AXY", "AYZ") lcs("AXYT", "AY")

In the above partial recursion tree, **lcs(“AXY”, “AYZ”) is being solved twice**. On drawing the complete recursion tree, it has been observed that there are many subproblems which are solved again and again. So this problem has Overlapping Substructure property and recomputation of same subproblems can be avoided by either using Memoization or Tabulation. The tabulation method has been discussed here.

A common point of observation to use memoization in the recursive code will be the **two non-constant arguments M and N **in every function call. The function has 4 arguments, but 2 arguments are constant which do not affect the Memoization. The repetitive calls occur for N and M which have been called previously. So use a 2-D array to store the computed **lcs(m, n) value at arr[m-1][n-1]** as the string index starts from 0. Whenever the function with the same argument m and n are called again, we do not perform any further recursive call and return arr[m-1][n-1] as the previous computation of the lcs(m, n) has already been stored in arr[m-1][n-1], hence reducing the recursive calls that happen more then once.

Below is the implementation of the Memoization approach of the recursive code.

## C++

`// C++ program to memoize ` `// recursive implementation of LCS problem ` `#include <bits/stdc++.h> ` `int` `arr[1000][1000]; ` `int` `max(` `int` `a, ` `int` `b); ` ` ` `// Returns length of LCS for X[0..m-1], Y[0..n-1] */ ` `// memoization applied in recursive solution ` `int` `lcs(` `char` `* X, ` `char` `* Y, ` `int` `m, ` `int` `n) ` `{ ` ` ` `// base case ` ` ` `if` `(m == 0 || n == 0) ` ` ` `return` `0; ` ` ` ` ` `// if the same state has already been ` ` ` `// computed ` ` ` `if` `(arr[m - 1][n - 1] != -1) ` ` ` `return` `arr[m - 1][n - 1]; ` ` ` ` ` `// if equal, then we store the value of the ` ` ` `// function call ` ` ` `if` `(X[m - 1] == Y[n - 1]) { ` ` ` ` ` `// store it in arr to avoid further repetitive ` ` ` `// work in future function calls ` ` ` `arr[m - 1][n - 1] = 1 + lcs(X, Y, m - 1, n - 1); ` ` ` `return` `arr[m - 1][n - 1]; ` ` ` `} ` ` ` `else` `{ ` ` ` `// store it in arr to avoid further repetitive ` ` ` `// work in future function calls ` ` ` `arr[m - 1][n - 1] = max(lcs(X, Y, m, n - 1), ` ` ` `lcs(X, Y, m - 1, n)); ` ` ` `return` `arr[m - 1][n - 1]; ` ` ` `} ` `} ` ` ` `// Utility function to get max of 2 integers ` `int` `max(` `int` `a, ` `int` `b) ` `{ ` ` ` `return` `(a > b) ? a : b; ` `} ` ` ` `// Driver Code ` `int` `main() ` `{ ` ` ` `memset` `(arr, -1, ` `sizeof` `(arr)); ` ` ` `char` `X[] = ` `"AGGTAB"` `; ` ` ` `char` `Y[] = ` `"GXTXAYB"` `; ` ` ` ` ` `int` `m = ` `strlen` `(X); ` ` ` `int` `n = ` `strlen` `(Y); ` ` ` ` ` `printf` `(` `"Length of LCS is %d"` `, lcs(X, Y, m, n)); ` ` ` ` ` `return` `0; ` `} ` |

*chevron_right*

*filter_none*

**Output:**

Length of LCS is 4

**3-D Memoization**

In the above program, the recursive function had only **two** arguments whose value were not constant after every function call. Below, an implementation where the recursive program has **three non-constant arguments** is done.

For e.g., Program to solve the standard Dynamic Problem LCS problem for three strings. The general recursive solution of the problem is to generate all subsequences of both given sequences and find the longest matching subsequence. Total possible combinations will be 3^{n}. Hence recursive solution will take **O(3 ^{n})**.

Given below is the recursive solution to the LCS problem:

## C++

`// A recursive implementation of LCS problem ` `// of three strings ` `#include <bits/stdc++.h> ` `int` `max(` `int` `a, ` `int` `b); ` ` ` `// Returns length of LCS for X[0..m-1], Y[0..n-1] ` `int` `lcs(` `char` `* X, ` `char` `* Y, ` `char` `* Z, ` `int` `m, ` `int` `n, ` `int` `o) ` `{ ` ` ` `// base case ` ` ` `if` `(m == 0 || n == 0 || o == 0) ` ` ` `return` `0; ` ` ` ` ` `// if equal, then check for next combination ` ` ` `if` `(X[m - 1] == Y[n - 1] and Y[n - 1] == Z[o - 1]) { ` ` ` ` ` `// recursive call ` ` ` `return` `1 + lcs(X, Y, Z, m - 1, n - 1, o - 1); ` ` ` `} ` ` ` `else` `{ ` ` ` ` ` `// return the maximum of the three other ` ` ` `// possible states in recursion ` ` ` `return` `max(lcs(X, Y, Z, m, n - 1, o), ` ` ` `max(lcs(X, Y, Z, m - 1, n, o), ` ` ` `lcs(X, Y, Z, m, n, o - 1))); ` ` ` `} ` `} ` ` ` `// Utility function to get max of 2 integers ` `int` `max(` `int` `a, ` `int` `b) ` `{ ` ` ` `return` `(a > b) ? a : b; ` `} ` ` ` `// Driver Code ` `int` `main() ` `{ ` ` ` ` ` `char` `X[] = ` `"geeks"` `; ` ` ` `char` `Y[] = ` `"geeksfor"` `; ` ` ` `char` `Z[] = ` `"geeksforge"` `; ` ` ` `int` `m = ` `strlen` `(X); ` ` ` `int` `n = ` `strlen` `(Y); ` ` ` `int` `o = ` `strlen` `(Z); ` ` ` `printf` `(` `"Length of LCS is %d"` `, lcs(X, Y, Z, m, n, o)); ` ` ` ` ` `return` `0; ` `} ` |

*chevron_right*

*filter_none*

**Output:**

Length of LCS is 5

The tabulation method has been shown *here*. On drawing the recursion tree completely, it has been noticed that there are many overlapping sub-problems which are been calculated multiple times. Since the function parameter has three non-constant parameters, hence a 3-D array will be used to memoize the value that was returned when **lcs(x, y, z, m, n, o)** for any value of m, n and o was called so that if **lcs(x, y, z, m, n, o)** is again called for the same value of m, n and o then the function will return the already stored value as it has been computed previously in the recursive call. arr[m][n][o] stores the value returned by the **lcs(x, y, z, m, n, o)** function call. The only modification that needs to be done in the recursive program is to store the return value of (m, n, o) state of the recursive function. The rest remains the same in the above recursive program.

Below is the implementation of the Memoization approach of the recursive code:

## C++

`// A memoize recursive implementation of LCS problem ` `#include <bits/stdc++.h> ` `int` `arr[100][100][100]; ` `int` `max(` `int` `a, ` `int` `b); ` ` ` `// Returns length of LCS for X[0..m-1], Y[0..n-1] */ ` `// memoization applied in recursive solution ` `int` `lcs(` `char` `* X, ` `char` `* Y, ` `char` `* Z, ` `int` `m, ` `int` `n, ` `int` `o) ` `{ ` ` ` `// base case ` ` ` `if` `(m == 0 || n == 0 || o == 0) ` ` ` `return` `0; ` ` ` ` ` `// if the same state has already been ` ` ` `// computed ` ` ` `if` `(arr[m - 1][n - 1][o - 1] != -1) ` ` ` `return` `arr[m - 1][n - 1][o - 1]; ` ` ` ` ` `// if equal, then we store the value of the ` ` ` `// fucntion call ` ` ` `if` `(X[m - 1] == Y[n - 1] and Y[n - 1] == Z[o - 1]) { ` ` ` ` ` `// store it in arr to avoid further repetitive work ` ` ` `// in future function calls ` ` ` `arr[m - 1][n - 1][o - 1] = 1 + lcs(X, Y, Z, m - 1, ` ` ` `n - 1, o - 1); ` ` ` `return` `arr[m - 1][n - 1][o - 1]; ` ` ` `} ` ` ` `else` `{ ` ` ` ` ` `// store it in arr to avoid further repetitive work ` ` ` `// in future function calls ` ` ` `arr[m - 1][n - 1][o - 1] = ` ` ` `max(lcs(X, Y, Z, m, n - 1, o), ` ` ` `max(lcs(X, Y, Z, m - 1, n, o), ` ` ` `lcs(X, Y, Z, m, n, o - 1))); ` ` ` `return` `arr[m - 1][n - 1][o - 1]; ` ` ` `} ` `} ` ` ` `// Utility function to get max of 2 integers ` `int` `max(` `int` `a, ` `int` `b) ` `{ ` ` ` `return` `(a > b) ? a : b; ` `} ` ` ` `// Driver Code ` `int` `main() ` `{ ` ` ` `memset` `(arr, -1, ` `sizeof` `(arr)); ` ` ` `char` `X[] = ` `"geeks"` `; ` ` ` `char` `Y[] = ` `"geeksfor"` `; ` ` ` `char` `Z[] = ` `"geeksforgeeks"` `; ` ` ` `int` `m = ` `strlen` `(X); ` ` ` `int` `n = ` `strlen` `(Y); ` ` ` `int` `o = ` `strlen` `(Z); ` ` ` `printf` `(` `"Length of LCS is %d"` `, lcs(X, Y, Z, m, n, o)); ` ` ` ` ` `return` `0; ` `} ` |

*chevron_right*

*filter_none*

**Output:**

Length of LCS is 5

**Note:** The array used to Memoize is initialized to some value (say -1) before the function call to mark if the function with the same parameters has been previously called or not.

## Recommended Posts:

- Memoization using decorators in Python
- Water Jug Problem using Memoization
- Edit Distance | DP using Memoization
- Longest Common Subsequence | DP using Memoization
- Count of cells in a matrix which give a Fibonacci number when the count of adjacent cells is added
- Greedy approach vs Dynamic programming
- Generate all unique partitions of an integer | Set 2
- Queries for bitwise OR in the given matrix
- Queries for bitwise AND in the given matrix
- Queries for bitwise AND in the index range [L, R] of the given array
- Queries for bitwise OR in the index range [L, R] of the given array
- Count the nodes whose sum with X is a Fibonacci number
- Distinct palindromic sub-strings of the given string using Dynamic Programming
- Maximum sum such that no two elements are adjacent | Set 2

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.